Twenty-year recovery of managed stand, in structure and composition, in boreal mixedwood stands of northwestern Quebec
Bibliographic record
Abstract
The natural disturbance-based management (NDBM) aims to maintain specific structural and compositional attributes of natural forests in managed stands. Operationally, NDBM relies on diversifying and adapting silvicultural practices, including partial harvesting (PC), to expand the range of options beyond that of simply clearcuts (CC). Established in 1998, the Sylviculture et Aménagement Forestier Écosystémique (SAFE) project evaluates this potential in hardwood, mixedwood, and coniferous stands in northwestern Québec, Canada. Our results confirmed a part of the NDBM objectives, i.e., PC allowed the maintenance of stands with mixed structure and composition, constituting an interesting complement to CC, which reset stand regeneration. However, PC did not accelerate the stand transition to later stages with less intensive harvesting or to earlier stages with more intensive harvesting. We essentially had an initial impact, delaying or stopping the stand evolution that dissipates over time and more quickly with less intensive harvesting. Furthermore, our results did not support the ability of PC to enhance the development of old-growth attributes like deadwood. Despite the 20-year horizon of this study, further field surveys will be required in the future to better understand the impact of different silvicultural treatments on forest productivity and biodiversity preservation throughout a forest rotation.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".